ValueError: Data cardinality is ambiguous ; Make sure all arrays contain the same number of samples

Question

I'm using this dataset from Kaggle, but I have this error:

ValueError: Data cardinality is ambiguous: x sizes: 8 y sizes: 8000 Make sure all arrays contain the same number of samples.

This is the full code:

import pandas as pd 
import numpy as np 
from sklearn.model_selection import train_test_split 
import tensorflow as tf

dataset = pd.read_csv('stunting1.csv')

dataset.Gender[dataset.Gender == 'Male'] = 1 
dataset.Gender[dataset.Gender == 'Female'] = 0

dataset.Breastfeeding[dataset.Breastfeeding == 'Yes'] = 1 dataset.Breastfeeding[dataset.Breastfeeding == 'No'] = 0

dataset.Stunting[dataset.Stunting == 'Yes'] = 1 
dataset.Stunting[dataset.Stunting == 'No'] = 0

x = dataset.drop(columns=['Stunting']) 
y = dataset['Stunting']

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)

model = tf.keras.models.Sequential()

x_train = np.array([np.array(val) for val in x_train]) 
y_train = np.array([np.array(val) for val in y_train])

x_test = np.array([np.array(val) for val in x_test]) 
y_test = np.array([np.array(val) for val in y_test])

model.add(tf.keras.layers.Dense(256, input_shape = x_train.shape, activation='sigmoid')) model.add(tf.keras.layers.Dense(256, activation='sigmoid')) 
model.add(tf.keras.layers.Dense(1, activation='sigmoid'))

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) model.fit(x_train, y_train, epochs=1000)

Could you help me? Should I make a shape but how?